Continuous business improvement initiatives in the past were predominantly driven by the notion of quality. Methods and systems were put in place to track defects in processes, and to generate KPI's (key performance indicators) based on quality driven metrics and measurements. It was well accepted across business sectors that quality is a huge differentiator, and higher quality leads to higher value. These notions were subsequently challenged by the need for business agility and the resulting imperative to function effectively in spite of imperfect information and/or processes.
Most methods and systems of the prior art do not lend themselves well to the context of modern businesses where big data is the norm and the causal impact of a real-time fluctuation in business environment can be immediate and significant. Businesses are struggling on multiple fronts to get on the bandwagon of this big data led frontier of business performance optimization. Notwithstanding the huge skills gap and immaturity of big data engineering technologies, the fundamental issue is the lack of a coherent framework for business performance optimization that incorporates big data and advanced analytics at its core. In addition, the immaturity of the big data engineering technologies, many companies are unable to systematically and consistently identify, develop, and create substantial new growth opportunities from the big data for the business interest. This may lead to a huge loss of business opportunity and other activities that generally build business slowly.